def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optimizationMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metInverted_QCD = addlabels(metInverted_QCD)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
fitOptions = "LRB"
# fitOptions = "RB"
# fitOptions = "IEB"
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit")
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VEB" # Chi2 Fit with Minos Error
# fitOptions = "IEB" # Chi2 Fit with Integral and Minos
# fitOptions = "VLEB" # Likelihood Fit with Minos Error
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili")
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VE" # Chi2 Fit with Minos Error
# fitOptions = "IE" # Chi2 Fit with Integral and Minos
# fitOptions = "VLE" # Likelihood Fit with Minos Error
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
style = tdrstyle.TDRStyle()
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metBasePP = plots.DataMCPlot(datasets, analysisPlusPlus+"/MET_BaseLineTauIdJets")
metBaseMP = plots.DataMCPlot(datasets, analysisMinusPlus+"/MET_BaseLineTauIdJets")
metBasePM = plots.DataMCPlot(datasets, analysisPlusMinus+"/MET_BaseLineTauIdJets")
metBaseMM = plots.DataMCPlot(datasets, analysisMinusMinus+"/MET_BaseLineTauIdJets")
# Rebin before subtracting
rebin = 20
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBasePP.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBaseMP.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBasePM.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBaseMM.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWKplusplus = metBasePP.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisPlusPlus+"/MET_BaselineTauIdJets")
metBase_EWKminusplus = metBaseMP.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisMinusPlus+"/MET_BaselineTauIdJets")
metBase_EWKplusminus = metBasePM.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisPlusMinus+"/MET_BaselineTauIdJets")
metBase_EWKminusminus = metBaseMM.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisMinusMinus+"/MET_BaselineTauIdJets")
invertedQCD = InvertedTauID()
invertedQCD.setLabel("noError")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.fitEWK(metBase_EWK)
invertedQCD.fitData(metBase_data)
normalizationWithEWK = invertedQCD.getNormalization()
invertedQCD.setLabel("plusplusError")
invertedQCD.fitEWK(metBase_EWKplusplus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKplusplus = invertedQCD.getNormalization()
invertedQCD.setLabel("minusplusError")
invertedQCD.fitEWK(metBase_EWKminusplus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKminusplus = invertedQCD.getNormalization()
invertedQCD.setLabel("plusminusError")
invertedQCD.fitEWK(metBase_EWKplusminus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKplusminus = invertedQCD.getNormalization()
invertedQCD.setLabel("minusminusError")
invertedQCD.fitEWK(metBase_EWKminusminus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKminusminus = invertedQCD.getNormalization()
invertedQCD.Summary()
print "Difference in normalization due to JES ++ variation",(normalizationWithEWK - normalizationWithEWKplusplus)/normalizationWithEWK
print "Difference in normalization due to JES -+ variation",(normalizationWithEWK - normalizationWithEWKminusplus)/normalizationWithEWK
print "Difference in normalization due to JES +- variation",(normalizationWithEWK - normalizationWithEWKplusminus)/normalizationWithEWK
print "Difference in normalization due to JES -- variation",(normalizationWithEWK - normalizationWithEWKminusminus)/normalizationWithEWK
def main():
# HISTONAME = "TauIdJets"
HISTONAME = "TauIdBtag"
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
bins = ["inclusive"]
# bins = ["4050","5060","6070","7080","80100","100120","120150","150"]
# bins = ["4050"]
for bin in bins:
invertedQCD.setLabel(bin)
if bin == "inclusive":
bin = ""
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLine"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, analysis+"/MET_Inverted"+HISTONAME+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_Inverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Inverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.fitEWK(metBase_EWK,"LR")
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
